CN105138791A - Evaluating method for excitation mode of eddy current sensor based on information entropy - Google Patents

Abstract

The invention relates to an optimal algorithm for excitation curves in the design of planar eddy current sensors. Using the entropy that describes the amount of information in information theory, through the finite element simulation results of the eddy current field induced by the excitation coil on the surface of the conductor, the distribution of the eddy current intensity in different directions is counted, and the information entropy of this distribution is obtained. This kind of information entropy is used as a selection criterion in the design of eddy current sensor. The present invention uses this information to compare the eddy current field under the excitation of a circle of circular coils and a circle of third-order Koch fractal curves on the information of different angle distributions and the eddy current field on a straight line perpendicular to the scanning direction. Information entropy of angular distribution.

Description

An evaluation method of excitation mode of eddy current sensor based on information entropy

technical field

The invention belongs to the technical field of electromagnetic non-destructive testing, and relates to a method for selecting an optimal excitation curve of a planar eddy current sensor for crack detection. It can be used as an evaluation method for the excitation performance of the sensor in the design of the eddy current sensor, and provides a feasible algorithm for the selection of the excitation curve in the design of the eddy current sensor.

Background technique

Eddy current testing plays an irreplaceable role in non-destructive testing during the production and service of mechanical products due to its advantages of low cost, fast speed and good environmental adaptability. The planar flexible eddy current sensor is a commonly used eddy current sensor as a curved surface component, and its excitation curve tends to develop in the direction of a single turn or several turns. However, the influence of the shape of the excitation curve on the performance of the eddy current sensor has to be considered.

The principle of eddy current sensor detecting cracks is the interaction between eddy current and cracks, that is, the eddy current is disturbed by cracks, changing the original flow path and distribution, so that cracks can be detected. However, when the angle between the direction of the eddy current and the direction of the crack, especially the micro-crack, is small, this disturbance will be extremely weak, and the sensitivity of the sensor to the crack will also decrease, resulting in a high probability of missed detection. Therefore, the excitation curve of the designed eddy current sensor is required to induce the eddy current on the surface of the conductor to be locally distributed in more directions, thereby increasing the probability of interaction between the eddy current and the crack.

The purpose of the present invention is to propose an optimization standard for the optimization of the excitation curve of the planar eddy current sensor.

Contents of the invention

In view of the above requirements, the present invention proposes a method using information entropy as a method for selecting an optimal excitation curve.

The steps taken by this method are as follows:

1. Establish a finite element model to calculate the distribution of eddy currents induced by excitation curves of the same size above the same conductor.

2. Sampling the eddy current density on the conductor surface at equal intervals to obtain the eddy current density vector. And use the following formula to calculate the magnitude and direction of the eddy current density at each point of the eddy current,

$\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; arctan</span>}\frac{{\mathrm{<span\; class="notranslate">\; J</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}}{{\mathrm{<span\; class="notranslate">\; J</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}}<span\; class="notranslate">\; |</span>$

$\mathrm{<span\; class="notranslate">\; J</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\sqrt{{\mathrm{<span\; class="notranslate">\; J</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; J</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

3. Set the included angle according to [0°,10°],(20°,30°],(30°,40°],(40°,50°],(50°,60°],(60°, 70°],(70°,80°],(80°,90°] into groups, and then follow the formula below

$\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; *</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}\frac{\mathrm{<span\; class="notranslate">\; J</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}\mathrm{<span\; class="notranslate">\; J</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

Find the probability of eddy current distribution in each interval.

4. Calculate the information entropy of the eddy current distribution at various angles by using the calculation formula on the information.

$\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; \&Omega;</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; log</span>}\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; )</span>$

By applying the above method to the information of eddy current distribution on different length line segments perpendicular to the scanning direction, the distribution of local eddy currents in different directions can be obtained. The greater the entropy value, the more directional the eddy current distribution is, and the stronger the corresponding interaction between eddy current and short cracks is, so the corresponding excitation curve has certain advantages.

Beneficial effect:

The invention proposes an optimal condition for using the information of the eddy current distribution as the excitation curve of the eddy current sensor for crack detection. An optimal method is provided for the selection and design of excitation curves of planar or planar flexible eddy current sensors.

Description of drawings

Figure 1: Algorithm steps.

Figure 2: Eddy current distribution diagram, (a) circular coil, (b) 3rd order Koch fractal curve.

Figure 3: Information entropy of eddy current distribution at different angles in the scanning direction:

(a) 60mm width Koch excitation; (b) 60mm width circular coil excitation; (c) 30mm width Koch excitation; (d) 30mm width circular coil excitation.

Detailed ways

In order to better illustrate the purpose and advantages of the present invention, the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

First, simulate the excitation results of equal-sized circles and third-order Koch curves. The simulation results are shown in Figure 2. The curves in the simulation are all ideal curves, and the specimens are made of equal-sized aluminum plates.

Then the eddy current distribution entropy on the straight line segments of different lengths perpendicular to the scanning direction in the scanning direction is calculated, as shown in Fig. 3 .

This embodiment only compares the local eddy current information entropy between equal-sized circles and third-order Koch curves. The method for selecting excitation curves of planar eddy current sensors for other types of crack detection can also be implemented using this method.

Claims (3)

1. one kind utilizes the method for information entropy to the selection method of the excitation curve of eddy current sensor, be characterized in the Eddy Distribution utilizing excitation curve to induce at conductive surface, add up perpendicular on scanning direction during different length, the probability of the distribution of eddy current in different angles, finally obtain the information entropy of this distribution, thus as eddy current sensor excitation curve select excellent condition.

2. claim is a kind of by the statistical method that eddy current distributes in different angles is: the angle first obtaining eddy current amplitude and eddy current and the horizontal direction that straight-line segment distributes,

By angle according to [0 °, 10 °], (20 °, 30 °], (30 °, 40 °], (40 °, 50 °], (50 °, 60 °], (60 °, 70 °], (70 °, 80 °], (80 °, 90 °] grouping, then according to formula below

Obtain the probability of each interval interior Eddy Distribution.

3. the computing method of the information entropy of the Eddy Distribution described in claim 1 are utilize in claim 2 p (the θ calculating acquisition _i), adopt formula below

$H\left(\mathrm{\&theta;}\right)=-\underset{x\&Element;\mathrm{\&Omega;}}{\&Sigma;}p\left(\mathrm{\&theta;}\right)\log p\left(\mathrm{\&theta;}\right)$

Calculate the information entropy that eddy current distributes in different angles, as a kind of evaluation method of the eddy current sensor excitation behavior of crack detection.